The abundance of bifidobacterium in relation to visceral obesity and serum uric acid

Gut microbiome has been shown to play a role in the development of obesity in recent studies. Most of these studies on obesity were based on the BMI classification criteria, which doesn't distinguish Visceral adipose tissue (VAT) from subcutaneous adipose tissue (SAT). Some studies showed that VAT has a higher risk of inducing metabolic diseases than SAT. This study focused on the visceral obesity defined by increased visceral fat area. The present study was designed to investigate the association of visceral obesity with gut predominant microbiota and metabolic status. This study included 372 healthy individuals from medical examination center in Shulan Hangzhou Hospital. Quantitative polymerase chain reaction (q-PCR) technique was used to detect ten kinds of gut predominant bacteria in fresh feces. Visceral fat area (VFA) was measured by the bioimpedance analyzer (INBODY720, Korea). The abundance of Bifidobacterium significantly decreased in the visceral obesity group. Compared with the lean group, Visceral obesity group had significantly higher levels of LDL, TG, FBG, serum uric acid (SUA) and lower levels of HDL. SUA was an independent impact factor for Bifidobacterium. SUA was negatively correlated with Bifidobacterium and positively correlated with VFA. In the mediation analysis, SUA showed significant mediation effect. SUA may be a mediating factor between decreased Bifidobacterium and increased VAT.

Use q-PCR to detect predominant gut microbiota. The information of PCR primers was shown in Supplementary Table S1 online. All oligonucleotide primers were synthesized by Gen Script (China). The ABI7500 real-time fluorescent PCR system (Applied Biosystems, USA) was used for the q-PCR amplification reaction. The amplification reaction contained 10uL of SYBRTM q-PCR master mix (Tong Chuang, China), 8 μL primers (0.2-0.6 μM), 2 μL template DNA, or 2 μL water (negative control), for a final volume of 20 μL. Each reaction was performed in triplicate, and the cycling threshold (ΔCt) < 0.5 between repetitions was required. Amplification was performed with the following temperature profiles: one cycle at pre-denaturation at 95 °C for 3 min, denaturation at 95 °C for 15 s, annealing and extension at 60 °C for 30 s, collection of fluorescence signals, a total of 40 cycles. The annealing and plate-reading temperatures for each primer pair are shown in Supplementary Table 1 online. The copy number of ribosomal DNA (rDNA) operons of targeted bacteria in crude DNA templates was determined by comparison with serially diluted plasmid DNA standards run on the same plate. Plasmid DNA standards were made from known concentrations of plasmid DNA that contained the respective amplicon for each set of primers. Bacterial count results were normalized to fecal bacteria count per gram (copies/g). Statistical analysis. SPSS software version 23.0 was used for statistical analysis. Normally distributed data were expressed as means and standard deviations. The t-test was used for comparison between groups, and the chi-square test was used for comparison of rates between groups. Non-normally distributed data were presented as median and interquartile range (IQR). Comparisons between groups were performed using the Mann-Whitney rank-sum test. Use Pearson correlation analysis to evaluate the correlation between gut microbiome and metabolic indicators. Multiple linear regression was used to analyze the independent correlation of gut microbiome with metabolic indicators after adjusting for confounding factors. For all tests, the statistical significance level was set at p < 0.05.

Results
Demographic and clinical metabolic characteristics. A total of 372 individuals were included, including104 subjects in the visceral obesity group (mean age 51.22 ± 10.60 years) and 268 subjects in the lean control group (mean age 49.39 ± 9.93 years). There were no significant differences in gender and age between two groups (P > 0.05). Visceral obesity group had significantly higher levels of BMI, SUA, TG, LDL, FBS and lower HDL (P < 0.05). (Table 1). Subjects were classified according to BMI as underweight (< 18. 5 kg/m 2 ), normal weight (18.5-23.9 kg/m 2 ), overweight (24.0-27.9 kg/m 2 ), and obese (≥ 28.0 kg/m 2 ) according to the Chinese standard 17 . There were 6 normal weight individuals in the visceral obesity group and 9 obese individuals in the lean control group.
Changes in the composition of the gut microbiome. Median count analysis showed that Bacteroides had the highest count of ten gut bacteria. Enterococcus had the lowest counts in both two groups. The counts of Eubacterium rectale and Clostridium butyricum in visceral obesity group was higher than lean group and the other eight bacteria in visceral obesity group were all lower than lean group (Fig. 1). The count of Bifidobacterium in the visceral obesity was significantly decreased, with a median of 6.08 × 10 4 copies/g, and the median of Bifidobacterium in lean group was 2.30 × 10 5 copies/g. The difference between the two groups was statistically significant (P < 0.05). Meanwhile, Bifidobacterium was negatively correlated with VFA (R = − 0.144, P < 0.01) (Fig. 2). www.nature.com/scientificreports/ SUA (R = − 0.176 P < 0.01) (Fig. 3) and positively correlated with HDL (R = 0.123, P < 0.05). Adjusting the age factor, the multiple linear regression showed that SUA was an independent impact factor for Bifidobacterial (β = − 0.151, P = 0.004) ( Table 2). Pearson correlation analysis showed that SUA was positively correlated with VFA (R = 0.195, P = 0.000 (Fig. 3). The PROCESS Marco for SPSS was used to analyze the mediation effect of SUA. The mediation analysis was performed using one independent variable (Bifidobacterium), one dependent variable (VFA), and one mediator (SUA). When SUA was included in the mediation model, the standardized   www.nature.com/scientificreports/ regression coefficient (β) for Bifidobacterium decreased from − 0.121 to − 0.089. It showed that SUA was the mediating factor between Bifidobacterium and VFA. Figure 4 illustrated the mediation model.

Discussion
This study showed that individuals with visceral obesity had lower level of Bifidobacterium, and Bifidobacterium was negatively correlated with VFA SUA was an independent impact factor for Bifidobacterial. SUA was negatively correlated with Bifidobacterium and positively correlated with VFA. The mediation analysis showed that SUA may be a mediating factor between decreased Bifidobacterium and increased VAT.
Recent studies have showed that there was gut microbiota dysbiosis in obese individuals. Special gut microbiome leaded to fat deposits. Transplant gut microbiota from mice with diet induced obesity to lean germ-free mice, the germ-free mice developed more fat deposits 18 . The vast majority of gut microbiota belong to four main families (phyla): Firmicutes, Bacteroidetes, Proteobacteria and Actinobacteria 19 . At the genus and species level, obesity individuals had higher count of Fusobacterium, Enterococcus, Prevotella, and lower counts of Faecalibacterium, Bifidobacterial than lean people [9][10][11][12][13] . In this study, the counts of Faecalibacterium prausnitzii were also decreased in visceral obesity, but the difference was not significant. The counts of Bifidobacterium significantly decreased in visceral obesity, that was the same as the conclusion of previous studies about obesity based on BMI criteria 12,13 . In a recent study published in 2022 using whole-genome shotgun sequencing, Bifidobacterium longum showed a strong correlation with VFA. Visceral fat was more closely correlated with the gut microbiome compared with BMI 20 . In our study, according to the Chinese BMI standard. There were 6 normal weight individuals in the visceral obesity group and 9 obese individuals in the lean control group. The metabolic status of these individuals was interesting. It needs further study to understand its mechanism.
Gut microbiome has been shown to play a role in the development of obesity. Gut microbes contribute to the pathogenesis of obesity by fermenting indigestible dietary polysaccharides, producing short-chain fatty acids, and regulating energy homeostasis 21 . Supplementation of Bifidobacterium breve to high-fat diet-induced obese mice, significantly dose-dependently suppressed the accumulation of body weight and epididymal fat, and improved the serum levels of total cholesterol, fasting glucose and insulin 22 . Epididymal fat in the study was visceral fat. And this study did not detect serum uric acid. In another study, Supplementation of probiotic yogurt with Bifidobacterium lactis Bb12 decreased the level of serum uric acid 23 . Our study found that SUA was an independent impact factor for Bifidobacterium. SUA was negatively correlated with Bifidobacterium and positively correlated with VFA. The mediation analysis showed that SUA may be a mediating factor between decreased Bifidobacterium and increased visceral adipose tissue.
SUA is a product of purine nucleotide metabolism, mainly derived from exogenous diet and endogenous nucleic acids. A certain level of serum uric acid is considered to be a beneficial antioxidant, but excess uric acid is associated with various diseases, such as hypertension 24 , diabetes 25 , cardiovascular disease 26 . Multiple epidemiological studies have shown a positive correlation between visceral fat and serum uric acid levels 27,28 . High levels of serum uric acid can increase insulin secretion, thereby promote fat synthesis 29 . Uric acid can also directly promote fat synthesis in hepatocytes via ER stress-induced activation of SREBP-1c 30 . This suggests that controlling serum uric acid levels may reduce the accumulation of visceral fat.
Besides exogenous diet, the gut microbiota also plays an important role in SUA level. About one-third of uric acid is excreted through the gut 31 . The gut microbiota has gradually become a new target to study the pathogenesis of hyperuricemia. Transplant fecal microbiota of diet induced hyperuricemia rats into recipient rats, SUA levels were significantly increased in recipient rats 32 . The abundance of gut Faecalibacterium prausnitzii, Clostridium butyrate-producing bacterium, Bifidobacterium decreased in the gout people 33 . As a member of probiotics, Lactobacillus can reduce SUA levels by synthesizing uric acid degrading enzymes 34 . The mechanism of the association between Bifidobacteria and SUA was unclear. Several studies have found that Bifidobacterium can reduce endotoxin levels, reduce intestinal mucosal permeability, and have a protective effect on the intestinal mucosal barrier 35,36 . Normal intestinal mucosal barrier helps to prevent the translocation of intestinal bacteria or bacterial lipopolysaccharide (LPS) into the blood. Elevated LPS levels in the blood increased the risk of hyperuricemia 37 .
Currently the main and widely used medications for lowering serum uric acid are xanthine oxidase inhibitors such as allopurinol. Some people cannot tolerate these medications because of its side effects. Bifidobacterium is a kind of probiotic that has been widely used. Its clinical safety has been proven, almost no side effects. In addition to direct supplementation with Bifidobacterium, supplementation with specific prebiotics, such as chicory, could also help to reduce serum uric acid levels 38 . By lowering SUA levels, we can lower the accumulation of visceral adipose tissue, further reduce the risk of metabolic diseases caused by visceral obesity.
This study also has several limitations. First, we only detected ten gut bacteria. There is an interaction between the vast gut microbiota. It's easy to miss other meaningful gut microbiota. We need to further use 16S rRNA gene amplicon sequencing to detect gut microbiota in people with visceral obesity. Assess the species composition of the gut microbiota and its relative abundance information in visceral obesity. Then use q-PCR to analyze the role of specific gut bacteria. Second, this study was cross-sectional data, which cannot determine any cause-effect relationships. The interactions between SUA and the gut microbiome are complex and dynamic. Therefore, more longitudinal study data are needed to help understand the mechanism of the link between SUA and Bifidobacterium, VFA.

Conclusions
This is a new perspective to study obesity and gut microbiota. Studying visceral obesity independently is beneficial to precisely prevent obesity-induced metabolic disease risk. The counts of Bifidobacterium significantly decreased in visceral obesity. SUA was negatively correlated with Bifidobacterium and positively correlated with VFA. SUA may be a mediating factor between decreased Bifidobacterium and increased visceral adipose tissue. Supplementation with Bifidobacterium might be a potential approach to reduce visceral adipose tissue.